PT - JOURNAL ARTICLE
AU - Gianola, Daniel
AU - van Kaam, Johannes B. C. H. M.
TI - Reproducing Kernel Hilbert Spaces Regression Methods for Genomic Assisted Prediction of Quantitative Traits
AID - 10.1534/genetics.107.084285
DP - 2008 Apr 01
TA - Genetics
PG - 2289--2303
VI - 178
IP - 4
4099 - http://www.genetics.org/content/178/4/2289.short
4100 - http://www.genetics.org/content/178/4/2289.full
SO - Genetics2008 Apr 01; 178
AB - Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from a theoretical perspective. It is argued that a nonparametric treatment may be needed for capturing the multiple and complex interactions potentially arising in whole-genome models, i.e., those based on thousands of single-nucleotide polymorphism (SNP) markers. After a review of reproducing kernel Hilbert spaces regression, it is shown that the statistical specification admits a standard mixed-effects linear model representation, with smoothing parameters treated as variance components. Models for capturing different forms of interaction, e.g., chromosome-specific, are presented. Implementations can be carried out using software for likelihood-based or Bayesian inference.